Abstract

This study examined the extent of low-energy reporting and its
relationship with demographic and lifestyle factors in women previously
treated for breast cancer.

This study used data from a large multisite clinical trial testing the
efficacy of a dietary intervention to reduce risk for breast cancer
recurrence (Women’s Healthy Eating and Living Study). Using the
Schofield equation to estimate energy needs and four 24-h dietary
recalls to estimate energy intakes, we identified women who reported
lower than expected energy intakes using criteria developed by G. R.
Goldberg et al. (Eur. J. Clin. Nutr.,
45: 569–581, 1991).

We examined data from 1137 women diagnosed with stage I, stage II, or
stage IIIA primary, operable breast cancer. Women were 18–70 years of
age at diagnosis and were enrolled in the Women’s Healthy Eating and
Living Study between August 19, 1995, and April 1, 1998, within 4 years
after diagnosis.

The Goldberg criteria classified about one-quarter (25.6%) as
low-energy reporters (LERs) and 10.8% as very LERs. Women who had a
body mass index >30 were almost twice (odds ratio, 1.95) as likely to
be LERs. Women with a history of weight gain or weight fluctuations
were one and a half times as likely (odds ratio, 1.55) to be LERs as
those who were weight stable or weight losers. Age, ethnicity, alcohol
intake, supplement use, and exercise level were also related to LER.

Characteristics (such as body mass index, age, ethnicity, and weight
history) that are associated with low-energy reporting in this group of
cancer survivors are similar to those observed in other populations and
might affect observed diet and breast cancer associations in
epidemiological studies.

Introduction

Estimates of
EI4
and the types of foods ingested are critical to studies relating
nutrition to health outcomes. These estimates rely on self-reports of
food intakes over varying periods of time. The most widely used methods
are diet recalls, where subjects report on foods consumed over the
previous 24 h; diet diaries, in which subjects record all foods
consumed on a daily basis; and food frequency questionnaires, which
require subjects to report food consumption patterns over longer
periods of time, from 1 month to 1 year or more. Because all these
methods have their limitations, there is no “gold standard” for
assessing dietary intake, which makes it difficult to assess
measurement error and its impact on study results.

Recently, researchers have begun to compare estimates of self-reported
EI with estimates of total energy expenditure to provide insight into
the validity of self-reported EIs. Using biological markers such as
doubly labeled water, or other methods to estimate total energy
expenditure, these studies have found that methods of self-reported
dietary assessment tend to underestimate EI (1, 2, 3, 4, 5, 6)
. This
phenomenon, termed “underreporting” or “low-energy reporting,”
may result from difficulties in accurately reporting food composition
and portion size; changing eating patterns to simplify reporting; not
reporting on “unusual” days of large consumption (i.e.,
weekends, parties); erroneous package labeling on locally produced
foods; changing eating patterns or reported consumption to be more
socially desirable; or not reporting complicated foods (mixed dishes)
or small items (bites and tastes; Refs. 7
and
8
).

Low-energy reporting is a concern in studies of diet-disease
relationships. Nonsystematic low-energy reporting could bias results
toward the null, whereas systematic low-energy reporting could bias
results if participant characteristics are related both to the
low-energy reporting and to the disease end point of interest. Prentice
(9)
has argued that the lack of relationship between
dietary fat and breast cancer may be a result of nonsystematic
underreporting of fat and EI.

Using techniques such as doubly labeled water to estimate potential
dietary measurement biases is impractical in large-scale studies. As a
surrogate, a number of large studies have estimated a measure of
expected EI using estimates of a participant’s energy expenditure and
basal metabolic rate (6, 10)
. These studies have
demonstrated considerable variability across participants in the
relationship between reported and expected EI. Low-energy reporting was
frequently observed and was more likely to occur among women (4, 11, 12, 13)
, among those categorized as overweight (11, 14, 15, 16, 17, 18, 19)
, among African Americans compared with Caucasians
(20)
, and among younger rather than older adults
(19, 20)
. Other demographic differences in underreporting
have also been observed (12, 16, 21)
. The differences
observed in these comparisons include not only the discrepancy between
self-reported intake and expected EI, but also differences in diet
composition (22)
, the number of foods reported (23, 24)
, portion sizes (23)
, and intake of specific
food groups (4)
.

The dietary assessment method chosen might influence underreporting: an
analysis of diet records in one study revealed that the reported number
of both foods and nutrients was considerably lower on the 4th day of
record-keeping than on the 1st day (25)
, suggesting that
participant burden contributed to underreporting. Several studies have
suggested that underreporting could be a concern when food intake is
assessed by 24-h recall (16, 26, 27)
, although Buzzard
et al.(27)
noted that skilled interviewers and
probing techniques could reduce the amount of underreporting
considerably.

To our knowledge, no studies have specifically examined underreporting
in breast cancer survivors by comparing EI with energy expenditure,
although some studies have estimated underreporting by comparing
different dietary assessment methods. In one study in women with
localized breast cancer (24)
, investigators found that EI,
number of food items, add-on foods, and supplements were underreported
more frequently in 24-h recalls than food records. Conversely, another
study in breast cancer survivors suggested that underreporting was a
greater problem with food records than with 24-h recalls
(27)
. This study examined 290 postmenopausal women with
localized breast cancer participating in a dietary intervention study.
The authors compared unannounced 24-h recalls conducted by telephone to
4-day food records over the 1st year of the study. Compared with the
24-h recalls, the 4-day food records overestimated the extent of fat
reduction in the low-fat diet intervention group by 41% at 6 months
and by 25% at 12 months.

In this study, we examined the prevalence of low-energy reporting among
a group of breast cancer survivors, participants of a large randomized
controlled trial investigating the effect of diet on breast cancer
recurrence. Low-energy reporting was a concern because baseline dietary
assessments suggested that this group had a lower mean percent energy
from fat (29%) than is reported in women in this age group in the
general population (28)
. We report differences in the
proportion of “LERs” across demographic and other health habits
categories. Furthermore, we investigate whether this low-energy
reporting is associated with lower reporting of a variety of specific
nutrients and food groups.

Materials and Methods

Population.

This study used data collected for the WHEL Study, a multisite clinical
trial testing whether a dietary pattern high in vegetables, fruits, and
fiber and low in fat will affect the course of breast cancer. Women
18–70 years of age at the time of diagnosis, who presented with early
breast cancer (stages I, II, or IIIA) within the previous 4 years were
recruited from seven clinical sites in the western United States (four
in California, one in Arizona, one in Texas, and one in Oregon). WHEL
participants had completed any prescribed chemotherapy before
enrollment; therefore, treatment-induced dietary differences
(29)
were eliminated as a confounder. Detailed eligibility
criteria are reported elsewhere (30)
, and results from a
feasibility study for this trial have been reported previously
(31, 32)
. The WHEL Study will recruit 3000 women and
randomly assign them to a dietary intervention group or to a control
group. Women enrolled in the WHEL Study between August 19, 1995, and
April 1, 1998 (n = 1137), are the focus of this report.
The WHEL Study protocol was approved by the Human Subjects Committee of
the University of California, San Diego, School of Medicine, and by the
Institutional Review Board at each clinical site.

Data Collection.

Information on smoking status and exercise level was obtained from the
Personal Habits Questionnaire, developed for the Women’s Health
Initiative clinical trial and observational study (30, 33)
. Adult weight history was also assessed with this
instrument. Demographic data were collected by a telephone screening
interview and study forms. All questionnaires were completed either
before or at a baseline clinic visit. At this clinic visit, staff
trained in the WHEL Study protocol weighed and measured women using
standard procedures (34)
and calculated BMI [weight
(kg)/height (m2)]. The final stage of the
baseline clinic visit was random assignment to the intervention or
control group. Furthermore, 729 of the women had completed a 1-year
clinic visit at the time of manuscript preparation, from which we could
track weight status subsequent to enrollment. Stable weight was defined
as a 1-year weight that was within 5% of the baseline weight.

Collection of Dietary Data.

A team of trained dietary assessors at the WHEL Study Coordinating
Center collected dietary assessments by telephone under the direction
of a dietary assessment supervisor, with consultation and oversight by
the director of nutrition services (V. N.). Dietary intake was
measured at baseline before randomization using four 24-h recalls
randomly selected to include recall of 2 weekdays (Monday and
Thursday) and 2 weekend days (Friday and Sunday) over a 3-week
period. The Minnesota Nutrition Data System software (University of
Minnesota, Minneapolis, MN) was used to collect dietary data, and the
University of Minnesota Nutrition database (version 2.92, 1997;
University of Minnesota) was used for nutrient analysis.

The study used several strategies to obtain accurate recalls of food
intake. Before enrollment, a registered dietitian trained study
participants to estimate serving sizes with food models and distributed
measuring cups and spoons, along with two-dimensional food models for
reference during the recalls. In addition, the Nutrition Data System
software uses an interactive multiple-pass method (35)
that improves dietary recall accuracy by providing several
opportunities during the interview to review the participant’s daily
diet at varying levels of detail. Computer-generated prompts ensure
that all assessors obtain detailed data about the type and amount of
food, as well as preparation methods. As an additional quality control
measure, at the end of every recall, the assessor verified the dietary
analysis for nutrient outliers and corrected any errors immediately.
Also, the dietary assessment supervisor verified all completed sets of
recalls and made corrections as needed. An estimate of inter-coder
variability was obtained quarterly by verifying the mean (SEM) EI and
intake of selected key nutrients by assessor for recalls completed
during that period. No significant differences between assessors were
noted.

All assessors successfully completed a 3-week training program
emphasizing standardized data collection, proper interviewing
technique, and efficient use of the dietary analysis software. This
initial training was followed by a week of scheduled recalls supervised
by an experienced assessor who was available to offer assistance and to
provide guidance as needed. During the following month, two full shifts
of recalls (at least eight) were taped and the dietary assessment
supervisor randomly selected at least four of these recalls for review.
Experienced assessors had their recalls taped for review quarterly. The
dietary assessment supervisor reviewed any discrepancies with each
assessor and recommended improvements as necessary. When problems with
accuracy and completeness of dietary data were noted, tape reviews were
scheduled weekly until the problem was resolved. All assessors were
female, ranging in age from 20–53 years.

Definition of Low-Energy Reporting.

To classify LERs, we used the methodology described by Goldberg
et al.(36)
, which derives cutoff limits for
plausible EIs depending on the sample size and number of days of
dietary data. Using this methodology, the EI:BMR ratio was calculated
where BMR was estimated by the Schofield equation (37)
,
using height and weight values measured at the baseline clinic visit.
We then compared these ratios to cutoffs for a single individual across
4 days of dietary data (1.06 for the 95% CI and 0.88 for the 99.7%
CI) to determine who was underreporting. These cutoffs determined
whether each individual’s EI could be a valid estimate for a 4-day
period “allowing for the known day to day and week to week
variability, and without having to postulate any systematic reduction
in intake which may have been caused by the measurement procedure”
(36)
. The cutoff, therefore, accounts for other reasons
respondents may have given for eating less on any day of report, such
as traveling, celebrating a special occasion, or being bored, stressed,
or not hungry (i.e., random, rather than systematic
underreporting).

Analysis.

Means and SDs were calculated for EIs and EI:BMR ratios. For EI and
EI:BMR ratio, differences between groups of demographic and behavioral
risk factors were quantified using a linear regression model for each
group. Using the Goldberg cutoffs, we classified anyone with an EI:BMR
ratio below 1.06 as a LER and anyone with a ratio below 0.88 as a VLER.
The VLER group is, thus, a subset of the LER group. Significant
differences in low-energy reporting between groups of behavioral and
demographic risk factors were tested using χ2
contingency table analysis. Logistic regression was used to examine
predictors of low-energy reporting, controlling for potential
covariates. t tests were used to test for differences in
grams or servings of specific nutrients or food groups between LER and
non-LER. For each nutrient and food group, we computed a percentage
difference for LER compared with non-LER.

Results

Table 1⇓
presents reported EIs, estimated EI:BMR ratios, and the percentage of
LERs and VLERs at baseline. Mean EI was 1699 kcal/day. As expected, EIs
decreased with age, and these differences were statistically
significant. Significant differences were also observed across
categories of BMI, with intakes generally increasing as BMI increased.
As levels of alcohol increased, EIs also increased, but the differences
disappeared when we subtracted alcohol calories from total energy. EIs
decreased with increasing exercise. There were marginal associations of
EI with supplement use (P = .10) and with weight change
at 1-year follow-up (P = .07). No differences in EI
were seen across ethnicity, adult weight history, or smoking status.

Reported EIs, Energy ratios (EI:BMR), and LER and VLERs in women at
risk for breast cancer recurrence, by demographic and behavioral
characteristics

The mean EI:BMR ratio was 1.28. The mean EI:BMR ratio by subgroup
ranged from a low of 1.07 in women with BMI ≥40 and 1.13 in African
American women to 1.58 in the oldest age group (70–74 years) and 1.57
in women with the lowest BMI (<18.5). Significant differences in the
EI:BMR ratio were observed for age, ethnicity, BMI, alcohol intake, and
adult weight history. Smoking status, exercise level, supplement use,
nonalcohol calories, and weight change at 1-year follow-up were not
associated with the EI:BMR ratio.

LERs accounted for 25.6% of the total sample, and VLERs accounted for
10.8% of the total sample. The percentage of persons classified as LER
reached substantial levels within certain subgroups, approaching 50%
in women with a BMI ≥40. The percentage of VLER observed was much
lower than the percentage of LER across all subgroups. The degree of
LER varied significantly by age, ethnicity, BMI, alcohol intake, and
adult weight history. The degree of VLER varied significantly by age,
BMI, exercise level, supplement use, alcohol intake, adult weight
history, and weight change at 1 year. In general, low-energy reporting
was highest among African American women and among those with BMI >35.

Table 2⇓
shows ORs of being classified as either LER or VLER, controlling for
covariates. These results suggest that a woman’s age was associated
with the risk of being both LER and VLER. Women, ages 35–59 years, had
an OR of 6.84 for LER and 11.99 for VLER, whereas women <35 years of
age had an OR of 19.44 for VLER. Alcohol intake was negatively
associated with both LER and VLER. BMI was related to LER but not VLER;
women with a BMI >30 had an OR of 1.95 for LER compared with those
with BMI <25. Women who gained weight or whose weight fluctuated had a
higher risk of LER (OR of 1.55) compared with women who lost weight or
were weight stable. Women who exercised moderately were at increased
risk of being a LER, and women who used five or more supplement
formulations a day were at decreased risk of being a VLER.

ORs and corresponding 95% CIs of being classified as a LER or a VLER,
by behavioral and demographic characteristics

Table 3⇓
presents mean values for specific nutrients and food groups for LER and
non-LER. Also presented in Table 3⇓
are the percentage differences
between LER and non-LER for each nutrient and food group. For all
nutrients, except β-carotene, mean values were significantly higher
for non-LER than LER. With reference to the percentage difference for
EI, the percentage differences for fat, alcohol, and sucrose were
higher, whereas the percentage differences for protein, fiber, vitamin
C, and β-carotene were lower, with β-carotene showing the smallest
percentage difference for nutrients between groups (−10.2). Mean
values for all food groups were significantly higher for non-LER than
LER, with percentage differences ranging from a low of −10.0 for
vegetable servings to −23.5 for legume servings.

Mean (SD) of LER and non-LER and percentage of decrease of LER from
non-LER for specific nutrients and food groups

Discussion

We report an average EI:BMR ratio of 1.28, indicating that a large
proportion of women in this population are reporting lower than
expected EIs. The Food and Agriculture Organization/World Health
Organization/United Nations University guidelines
(38)
suggest that the average daily EI:BMR ratio for women
engaged in light work is 1.56, and 1.35 is recommended as the lowest
habitual value for the EI:BMR ratio compatible with a normal lifestyle
(26)
.

Although this study estimated a low EI:BMR ratio, it is consistent with
other published data. Black et al.(10)
, in
their review of 37 published dietary studies on adults, found the
average estimated EI:BMR ratio for women to be 1.37, and many of the
studies reported an estimated EI:BMR ratio equal to or less than our
finding of 1.28 (36)
. They also examined the EI:BMR ratio
by dietary method and found that the average estimated EI:BMR ratio
from 17 studies, all using 24-h dietary recalls similar to the WHEL
Study methodology, was 1.31.

Furthermore, the average EIs that we found in this study are consistent
with those reported in large nationwide surveys. EIs reported by women
from 24-h dietary recalls in the Third National Health and Nutrition
Examination Survey, Phase I (1988–1991; Ref. 28
) were
within 4% of those reported in our study across all age groups.

This study used telephone interviewing to conduct the 24-h dietary
recalls, which could influence comparisons with studies using
face-to-face dietary assessment methods. However, a recent large-scale
study using multiple-pass 24-h dietary recalls in 700 women, ages
20–49 (39)
, concluded that telephone interviewing was a
valid alternative to face-to-face interviews for collecting 24-h
dietary recall data.

One limitation to our methodology is that our measure of energy
expenditure is not based on more precise methods, such as doubly
labeled water measurements or indirect calorimetry. However,
measurement error does occur with the doubly labeled water approach as
well and is compounded by estimates of various physiological factors
and calculation assumptions. The major component of total energy
expenditure is BMR, which is most strongly determined by the fat-free
mass (40)
. This study used body weight as a surrogate for
fat-free mass to estimate BMR, which could lead to significant error.

One possible explanation for the apparent high level of low-energy
reporting observed could be that the Schofield equation overestimates
the BMR for this group of cancer survivors within 1–4 years of
diagnosis. Demark-Wahnefried et al.(41)
studied energy balance in breast cancer patients undergoing
chemotherapy and reported that BMR decreased during treatment but
returned to pretreatment levels at the end of treatment. However, lean
body mass, which is the major determinant of BMR, also decreased during
treatment but did not return to pretreatment levels in a small subgroup
of women measured 1 year later. They hypothesized that decreases in
lean body mass may contribute to the weight gain observed in women
diagnosed with breast cancer. A lower lean body mass among women in our
study may have contributed to having an estimated BMR that is higher
than the true value.

It is important to note that the Goldberg methodology (36)
used in this study provides a conservative estimate of low-energy
reporting, because it does not identify those with high-energy needs
(i.e., the very active) who might be low-energy reporting as
well. It identifies only those persons who are at the extreme end of
the distribution and report intakes that are not feasible to sustain a
sedentary lifestyle.

The Goldberg methodology (36)
also assumes that energy
needs are stable and that EI reflects current needs. To examine whether
weight change influenced our results, we divided a subgroup of women
for whom we had weight measurements at 1 year after baseline into three
groups: weight stable (within 5% of body weight), weight gainers
(>5% of body weight), and weight losers (<5% body weight). We
assumed that weight losers would be more likely to be labeled as LERs,
because EI during weight loss would be less than their baseline energy
needs. On the other hand, we assumed that weight gainers would be less
likely to be labeled LERs, because energy needs to promote weight gain
would be higher than baseline weight energy needs. However, our data
showed no differences in low-energy reporting rates among these three
groups of women, suggesting that the weight changes were not occurring
during baseline data collection. Furthermore, 72% of the women in this
subsample were weight stable; therefore, the majority satisfied the
condition of intakes in balance with energy expenditure.

Our analysis showed that women who consumed >6 grams of alcohol a day
were less likely to report low-EIs than those who consumed no alcohol
(Table 1)⇓
. This finding is intriguing and has been reported in one
other study (21)
. One interpretation is that persons who
report alcohol intake are more likely to also report other less
socially desirable dietary constituents. However, when we examined the
data stratified by alcohol intake for nonalcohol energy only, the
differences seen with total energy disappeared, demonstrating that
alcohol drinkers and nonalcohol drinkers reported similar nonalcohol
EI. As expected, because nonalcohol EI did not differ significantly
between categories of alcohol intake, neither did the rate of
low-energy reporting differ between categories of alcohol intake. It
seems that alcohol energy reported by heavier drinkers increased their
reported EI above those who do not drink, and the discrepancy between
their energy needs based on the Schofield equation and their total
reported EI is less than for nondrinkers.

Table 3⇓
shows that compared with energy the nutrients and food groups
that are less socially desirable such as fat, sucrose, and alcohol are
underreported to a greater extent than the socially desirable nutrients
such as protein and β-carotene. Other investigators have reported
similar findings (18, 23)
. Hietmann and Lissner
(18)
and Krebs-Smith et al.(23)
have shown that participants who underreport seem to differentially
underreport both sweet and savory snack foods.

If the desire to conform to social norms is related to underreporting,
especially with regard to nutrients believed to affect breast health,
then our power to detect associations between those nutrients and
either breast cancer incidence or prognosis is severely diminished due
to exposure misclassification. Results from epidemiological studies
examining the association between dietary factors and risk for primary
or recurring breast cancer must be interpreted with this awareness.

In summary, the results from this study support conclusions from the
majority of other studies investigating underreporting, showing
substantial underreporting of EIs among women and demonstrating that
body weight is one of the major determinants of reporting low EIs.
Having a previous diagnosis of breast cancer does not differentiate
these women from the general population of women, either with regard to
the prevalence of low-energy reporting or predictors of low-energy
reporting. We also found that age, alcohol intake, and supplement use
were related to reporting low EIs. This is the first study that has
reported on a relationship between low-energy reporting and supplement
use. Future studies examining dietary determinants of cancer
recurrence must account for these apparent biases in energy
reporting.

Footnotes

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

↵1 Supported in part by Grant CA69375 from the
National Cancer Institute, by the Walton Family Foundation, and in
small part by NIH Grants M01-R0070 and M01-RR00827.